Mobile platforms offer increasingly sophisticated capabilities associated with the motion and/or position location sensing of the mobile platform. New software applications, such as, for example, those related to personal productivity, collaborative communications, social networking, and/or data acquisition, may utilize motion and/or position sensors to provide new features and services to consumers.
Such motion and/or position determination capabilities may be provided using Satellite Positioning Systems (SPS), such as a global positioning system (GPS). However, position determinations based on SPS measurements alone may have inherent errors on the order of a few meters. Such accuracy may not be sufficient for certain applications. In mobile platforms, position accuracy can be improved by augmenting measurements derived from SPS with other available sensors/systems.
One such system that may be available to the mobile platform is a Visual-Inertial Odometry (VIO) system. Certain example VIO systems may use information from consecutive or otherwise temporally-separated images obtained from one or more digital cameras to estimate displacements of the mobile platform from one or more previous positions. These displacement estimates may, in certain instances, be of very high quality, e.g., being off by less than 1%. While the errors in the displacement estimates of some VIO systems may be relatively small, they may accumulate over time, which may lead to a significant drift in position estimates. This may be particularly problematic in vehicular applications, such as Advanced Driver Assistance Systems (ADAS) or robotics applications, such as drone navigation, where a large drift may interfere with proper route planning and/or mapping.